Exact asymptotic behavior of magnetic stripe domain arrays

Johansen, T. H.; Pan, Alexey V.; Galperin, Y. M.

doi:10.1103/physrevb.87.060402

Cited by 21 publications

(19 citation statements)

References 29 publications

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“…Besides, we show that the three models behave in the same way at sufficiently low temperatures, as expected, and analyze the behaviour of the stripe widths and asymmetry as a function of magnetic fields. At low temperatures our results are compared with recent analytic work on a sharp model at T = 0 showing a very good agreement with theoretical predictions 18 .…”

Section: Introductionsupporting

confidence: 77%

Inverse transition in the dipolar frustrated Ising ferromagnet: The role of domain walls

2014

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We present a theoretical study aimed to elucidate the origin of the inverse symmetry breaking transition observed in ultrathin magnetic films with perpendicular anisotropy. We study the behavior of the dipolar frustrated Ising model in a mean field approximation as well as two other models with simple domain walls. By a numerical analysis we show that the internal degrees of freedom of the domain walls are decisive for the presence of the inverse symmetry breaking transition. In particular, we show that in a sharp domain wall model the inverse transition is absent. At high temperatures the additional degrees of freedom of the extended domain walls increase the entropy of the system leading to a reduction of the free energy of the stripe phase. Upon lowering the temperature the domain walls become narrow and with the corresponding degrees of freedom effectively frozen, which eventually induces an inverse transition to the competing homogenous phase. We also show that, for growing external field at constant temperature, the stripe width grows strongly when approaching the critical field line and diverges at the transition. These results indicate that the inverse transition is a continuous phase transition and that the domain wall profiles as well as the temperature has little effect on the critical behavior of the period of the domain as function of the applied field.

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Section: Introductionsupporting

confidence: 77%

Inverse transition in the dipolar frustrated Ising ferromagnet: The role of domain walls

2014

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“…In particular, unique spin textures in two-dimensional magnetic systems, such as magnetic stripe domains (1)(2)(3)(4)(5) and magnetic skyrmions (6)(7)(8)(9)(10), and their connection to various magnetic Hamiltonian parameters have been extensively studied. To quantitatively understand the properties of these magnetic structures, not only experimental methods (11)(12)(13)(14) but also various theoretical methods (15)(16)(17)(18)(19)(20)(21) including numerical calculations have been used. In general, the results from numerical calculations or analytical approaches cannot be directly compared with experimental results, since not all experimental factors can be considered in theoretical calculations.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Hamiltonian parameter estimation using deep learning techniques

et al. 2020

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Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.

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“…From the analysis of the experimental data, Fig. 1 d, we extract a saturation magnetization of B s = 21.3 mT (critical field B c = 11.4 mT 29 ). More technical details can be found in “Methods”.…”

Section: Resultsmentioning

confidence: 99%

Thermally active nanoparticle clusters enslaved by engineered domain wall traps

2021

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The stable assembly of fluctuating nanoparticle clusters on a surface represents a technological challenge of widespread interest for both fundamental and applied research. Here we demonstrate a technique to stably confine in two dimensions clusters of interacting nanoparticles via size-tunable, virtual magnetic traps. We use cylindrical Bloch walls arranged to form a triangular lattice of ferromagnetic domains within an epitaxially grown ferrite garnet film. At each domain, the magnetic stray field generates an effective harmonic potential with a field tunable stiffness. The experiments are combined with theory to show that the magnetic confinement is effectively harmonic and pairwise interactions are of dipolar nature, leading to central, strictly repulsive forces. For clusters of magnetic nanoparticles, the stationary collective states arise from the competition between repulsion, confinement and the tendency to fill the central potential well. Using a numerical simulation model as a quantitative map between the experiments and theory we explore the field-induced crystallization process for larger clusters and unveil the existence of three different dynamical regimes. The present method provides a model platform for investigations of the collective phenomena emerging when strongly confined nanoparticle clusters are forced to move in an idealized, harmonic-like potential.

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Exact asymptotic behavior of magnetic stripe domain arrays

Cited by 21 publications

References 29 publications

Inverse transition in the dipolar frustrated Ising ferromagnet: The role of domain walls

Inverse transition in the dipolar frustrated Ising ferromagnet: The role of domain walls

Magnetic Hamiltonian parameter estimation using deep learning techniques

Thermally active nanoparticle clusters enslaved by engineered domain wall traps

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